Image processor

ABSTRACT

A system and method for making an image processor. A system for processing an image may include a target image processing element, a distorted image calculating element coupled to the target image processing element, an eccentricity estimator coupled to the distorted image calculating element, an eccentricity compensator coupled to the distorted image calculating element, a distorted foveated image modeler coupled to the eccentricity compensator, a log-polar image generator coupled to the eccentricity compensator, and an unreliable feature omitter coupled to the eccentricity compensator. Methods to make the foregoing system are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application claims priority to U.S. Provisional Application No. 60/875,731; filed Dec. 19, 2006, titled “A Model of Eccentricity Compensator for Fovea Sensor”; and No. 60/875,740, filed Dec. 19, 2006, titled “A Model of Rotation-, Scale-, and Transition-invariant Feature Extractor from Space-invariant Image.”

INCORPORATION BY REFERENCE

References cited within this application, including patents, published patent applications other publications, and the U.S. Provisional Application Nos. 60/875,731 and 60/875,740; both filed Dec. 19, 2006, are hereby incorporated by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND

1. Field

This disclosure is generally related to signal processors and in particular to an optical image processor.

2. Description of Related Art

Log-polar mapping, biologically-inspired by analytic formulation of cortical mapping of the primate visual system, exists as a method that reduces image data size drastically but also gives rotation- and scale-invariant (RS-invariant) property. It translates rotation and scaling into translation in two orthogonal directions. This property is applicable for an active vision system where a camera is motorized for changing its gaze position. That is, such system utilizes a log-polar image for implementing rotation-scale- and translation-invariant (RST-invariant) image matching by regarding control of the camera's gaze position at a target as equivalent with translation. In order to acquire the log-polar image, a special CCD or CMOS log-polar chip, that is, a retina-like fovea sensor exists, where a united size of photosensitive elements increases as going to periphery. This fovea sensor has been incorporated into some active vision systems for the purpose of image matching. As another fovea sensor, a wide-angle foveated lens exists for acquiring the log-polar image by distorting a projected image geometrically.

A specially-made optics, such as a Wide-Angle Foveated (WAF) lens, exists where a projected image is distorted geometrically. It combines the special lens with a commercially available conventional linear-coordinate vision chip, where photosensitive elements are arranged uniformly.

BRIEF SUMMARY

Embodiments of the present disclosure provide a system and method for making a image processor.

Briefly described, in architecture, one embodiment of the system, among others, can be implemented as follows.

A system for processing an image may include a target image processing element, a distorted image calculating element coupled to the target image processing element, an eccentricity estimator coupled to the distorted image calculating element, an eccentricity compensator coupled to the distorted image calculating element, a distorted foveated image modeler coupled to the eccentricity compensator, a log-polar image generator coupled to the eccentricity compensator, and an unreliable feature omitter coupled to the eccentricity compensator.

The present disclosure can also be viewed as providing a method of processing an image. The method may include providing a target image, calculating a distorted image from the target image, estimating an eccentricity between the target image and the distorted image, compensating for the eccentricity between the target image and the distorted image; modeling a distorted foveated image, generating a log-polar image, and omitting an unreliable feature.

Other systems, methods, features, and advantages of the present invention will be, or will become apparent, to a person having ordinary skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features, and advantages included within this description, be within the scope of the present disclosure, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Many aspects of the disclosure can be better understood with reference to the following drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating principles of the present invention. Moreover, in the drawing, like-referenced numerals designate corresponding parts throughout the several views.

FIG. 1 illustrates a coordinate system of an image related to an embodiment of the present disclosure.

FIG. 2 illustrates an Advanced Wide-Angled Foveated (AdWAF) image and a linear coordinate image.

FIG. 3( a-f) illustrates a Target image (I) in part (a), distorted foveated image (DF) in part (b), polar images P₁ in part (c) and P₂ in part (d), compensated log-polar image (CLP) in part (e), and undistorted foveated image (UDF) in part (f), in each eccentricity θ_(ε)=0[°], 18.69[°], and 34.08[°] from the left.

FIG. 4 illustrates a model log-polar image LP and coordinates of the log-polar image.

FIG. 5 illustrates a comparison of a Root Mean Squared Error (RMSE) E among P₁, P₂ and CLP versus θ_(ε).

FIG. 6 illustrates a distribution of digitized errors, Δ_(η) (left) and Δξ, (right) of the image CLP.

FIG. 7 illustrates an RMSE before and after unreliable feature omission (UFO) in a presence of white Gaussian noise (WGN).

FIG. 8 illustrates an RMSE when a regulating parameter M_(f) is changed.

FIGS. 9( a-i) illustrate CLP images before and after UFO when θ_(ε)=0[°], 18.69[°], and 34.08[°] from the left.

FIGS. 10( a-b) illustrate CF images under an f_(CF) mapping.

FIG. 11 illustrates a flowchart for an RST-invariant feature acquisition from a space-variant image.

FIG. 12 illustrates a processing flow of an FMT.

FIG. 13 illustrates an RMSE between the reconstructions f_(re) and f_(m) _(—) _(re) under conditions of N_(η)×N_(ξ)=256×128, 128×64 and 64×32.

FIG. 14 illustrates an RMSE of UDF, U, f_(re) of UDF and f_(re) of U, from f_(m) _(—) _(re).

FIG. 15( a-f) illustrate UDF, U, f_(re) of UDF and f_(re) of U when θ_(ε)=0[°], 18.69[°], and 34.08[°] (from the left).

FIG. 16 illustrates a flowchart of a method of the present disclosure.

FIG. 17 illustrates a block diagram of an exemplary embodiment of a system of the present disclosure.

DETAILED DESCRIPTION

The present disclosure relates to a system and method for making an image processor.

As a person having an ordinary skill in the art would appreciate, an arrow entering a block or a symbol indicates an input and an arrow leaving a block or a symbol indicates an output. Similarly, connections described below may be of any electromagnetic type, such as electrical, optical, radio-frequency, and magnetic.

The present disclosure describes an image or a signal from which rotation-, scale-, and translation-invariant features are extracted.

I. Eccentricity Compensator A. Calculation of Wide-Angle Distorted Foveated Image

FIG. 1 illustrates a coordinate system of an image related to an embodiment of the present disclosure. A distorted foveated (DF) image is calculated from a target image I. The DF simulates an input image by the fovea sensor. The following definition assumes that the image I is on the object plane with eccentricity θ_(ε) in the φ_(ε)-direction. That is, the θ_(ε) is an angle between the optical axis and the straight line from the optical center to the target image center, as shown in FIG. 1. The target image center is the origin of the image I. Discrete coordinates, (x_(d), y_(d)), of the image I are calculated from its continuous coordinates (x,y) using an element size (δ_(x), δ_(y)).

$\begin{matrix} \left\{ {\begin{matrix} {x_{d} = \left\lfloor {{x/\delta_{x}} + 0.5} \right\rfloor} \\ {y_{d} = \left\lfloor {{y/\delta_{y}} + 0.5} \right\rfloor} \end{matrix}.} \right. & (1) \end{matrix}$

A direction, (θ, φ), from a point (x, y) to the optical center O_(c), is represented in Equ. (2).

$\begin{matrix} \left\{ {\begin{matrix} {\theta = {\tan^{- 1}\left\{ {\sqrt{\left( {x + {ɛ\; \cos \; \varphi_{ɛ}}} \right)^{2} + \left( {y + {ɛ\; \sin \; \varphi_{ɛ}}} \right)^{2}}/L} \right\}}} \\ {\varphi = {\tan^{- 1}\left\{ {\left( {y + {ɛ\; \sin \; \varphi_{ɛ}}} \right)/\left( {x + {ɛ\; \cos \; \varphi_{ɛ}}} \right)} \right\}}} \end{matrix},} \right. & (2) \end{matrix}$

where L is a length from the optical center to the object plane, and ε is positional eccentricity on the object plane.

ε=L tan θ_(ε)  (3)

Continuous coordinates (x′, y′) of the image DF are represented as

$\begin{matrix} \left\{ {\begin{matrix} {x^{\prime} = {\alpha_{1}\left( {{{r(\theta)}\cos \; \varphi} - {{r\left( \theta_{ɛ} \right)}{\cos \left( \varphi_{ɛ} \right)}}} \right)}} \\ {y^{\prime} = {\alpha_{1}\left( {{{r(\theta)}\sin \; \varphi} - {{r\left( \theta_{ɛ} \right)}{\sin \left( \varphi_{ɛ} \right)}}} \right)}} \end{matrix},} \right. & (4) \end{matrix}$

where r(θ) shows the image height, determined by the foveation model, versus incident angle θ, α₁ is magnification of the image, (θ_(ε), φ_(ε)) shows a direction from the target image center to the optical center. Thus, the origin of the coordinates (x′, y′) corresponds to the direction (θ_(ε), φ_(ε)). Discrete coordinates, (x_(d)′, y_(d)′), of the image DF are calculated from the (x′, y′) by an element size (ε′_(x), δ′_(y)).

$\begin{matrix} \left\{ {\begin{matrix} {x_{d}^{\prime} = \left\lfloor {{x^{\prime}/\delta_{x}^{\prime}} + 0.5} \right\rfloor} \\ {y_{d}^{\prime} = \left\lfloor {{y^{\prime}/\delta_{y}^{\prime}} + 0.5} \right\rfloor} \end{matrix},} \right. & (5) \end{matrix}$

The present disclosure uses a foveation model, such as an Advanced Wide Angle Foveated (AdWAF) model, in order to calculate the image DF from which a log-polar image can be acquired. The AdWAF model uses both linear coordinates and logarithmic coordinates in both planar projection and spherical projection. The field of view (FOV) is divided into 4 areas, that is, fovea (0≦θ≦θ₀), para-fovea (θ₀≦θ≦θ₁), near-periphery (θ₁≦θ≦θ₂), and periphery (θ₂≦θ≦θ_(max)). FIG. 2 compares a simulated AdWAF image and a linear-coordinate image by pinhole camera (PHC) lens model. In FIG. 2, the boundaries of FOV, that is, θ₀, θ₁, θ₂ and θ_(max), are 9.826[°], 19.107 [°], 34.715[°], and 60.0[°] respectively. Intensity is changed in order to see each boundary easily. The image height r(θ) of the AdWAF model is defined in the following.

AdWAF Model:

if 0≦θ≦θ₀,

r=r_(max)c₀f₁ tan θ,  (6)

where f₁ is a focal length for planar projection, else if θ₀≦θ≦θ₁ (inner bright part in FIG. 2),

r=r _(max) {c ₁ log_(a) f ₁ tan θ+d₁},  (7)

where a basis α is represented as α=exp(1/f₁ tan θ₀), else if θ₁≦θ≦θ₂,

r=r _(max) {c ₂ log_(b)(f ₂θ)+d ₂},  (8)

where f₂ is a focal length for spherical projection, and a basis b is represented as b=exp(1/f₂θ₂), else if θ₂≦θ≦θ_(max),

r=r _(max) {c ₃ f ₂ θ+d ₃},  (9)

where r_(max) is the maximum image height when θ=θ_(max), c_(i) (i=0, 1, 2, 3) is a scale modification factor for adjusting the height, and d_(i) (i=1, 2, 3) is calculated by continuity of the image height and its magnification.

PHC Lens:

$\begin{matrix} {r = {\frac{r_{\max}c_{0}}{\tan \; \theta_{\max}}\tan \; \theta \mspace{45mu} {\left( {0 \leq \theta \leq \theta_{\max}} \right).}}} & (10) \\ {L = {\frac{r_{\max}}{\tan \; \theta_{\max}}.}} & (11) \end{matrix}$

B. Modeling Compensated Log-Polar Image:

As shown in FIG. 3, the image DF is highly distorted. Continuous coordinates, (x″, y″), of the undistorted foveated image UDF (that is, with linear coordinates) are calculated by Equ. (12). The origin of the (x″, y″) corresponds to that of the image I.

$\begin{matrix} \left\{ {\begin{matrix} {x^{\prime\prime} = {\alpha_{2}\left( {{L\; \tan \; {\theta cos}\; \varphi} - {ɛcos\varphi}_{ɛ}} \right)}} \\ {y^{\prime\prime} = {\alpha_{2}\; \left( {{L\; \tan \; {\theta sin}\; \varphi} - {ɛ\; \sin \; \varphi_{ɛ}}} \right)}} \end{matrix},} \right. & (12) \end{matrix}$

where α₂ is magnification. Discrete coordinates (x_(d)″, y_(d)″) of the image UDF are calculated from the (x″, y″) by an element size (δ″_(x), δ″_(y)),

$\begin{matrix} \left\{ {\begin{matrix} {x_{d}^{\prime\prime} = \left\lfloor {{x^{\prime\prime}/\delta_{x}^{\prime\prime}} + 0.5} \right\rfloor} \\ {y_{d}^{\prime\prime} = \left\lfloor {{y^{\prime\prime}/\delta_{y}^{\prime\prime}} + 0.5} \right\rfloor} \end{matrix},} \right. & (13) \end{matrix}$

where (δ″_(x), δ″_(y)) is an element size of this image.

Continuous coordinates, (η, ξ, of the compensated log-polar image CLP (that is, a remapped log-polar image after correcting the deformation caused by the eccentricity) are calculated from the coordinates (x″,y″) as in Equ. (14).

$\begin{matrix} \left\{ {{\begin{matrix} {\eta = {\tan^{- 1}\left( {y^{\prime\prime}/x^{\prime\prime}} \right)}} \\ {\xi = {\alpha_{3}\left( {{r(\Theta)} - r_{0}} \right)}} \end{matrix}\mspace{65mu} \left( {\theta_{0} \leq \Theta \leq \theta_{1}} \right)},} \right. & (14) \end{matrix}$

where r₀ is a radius from which the log-polar image starts, α₃ is magnification of the image, and Θ is defined as

$\begin{matrix} {{\Theta = {\tan^{- 1}\left( \frac{\sqrt{x^{''2} + y^{''2}}}{L^{''}} \right)}},} & (15) \end{matrix}$

where L″=α₂L. Discrete coordinates (η_(d), ξ_(d)) of the image CLP are

$\begin{matrix} \left\{ {{\begin{matrix} {\eta_{d} = \left\lfloor {{\eta/\delta_{\eta}} + 0.5} \right\rfloor} \\ {\xi_{d} = \left\lfloor {{\xi/\delta_{\xi}} + 0.5} \right\rfloor} \end{matrix}\mspace{14mu} \left( {\theta_{0} \leq \Theta \leq \theta_{1}} \right)},} \right. & (16) \end{matrix}$

where (δ_(η), δ_(ξ)) is an element size of this polar image, and the maximum integer of η_(d) is replaced with 0 (that is, the discrete value η_(d) corresponds to 0≦η<2π).

Cartesian coordinates, (x′″, y′″), of the compensated foveated image CF are represented as

$\begin{matrix} \left\{ \begin{matrix} {x^{\prime\prime\prime} = {\alpha_{4}{r(\Theta)}\cos \; \eta}} \\ {{y^{\prime\prime\prime} = {\alpha_{4}{r(\Theta)}\sin \; \eta}},} \end{matrix} \right. & (17) \end{matrix}$

where α₄ is magnification of the image. Discrete coordinates of the image CF are

$\begin{matrix} \left\{ \begin{matrix} {x_{d}^{\prime\prime\prime} = \left\lfloor {{x^{\prime\prime\prime}/\delta_{x}^{\prime\prime\prime}} + 0.5} \right\rfloor} \\ {{y_{d}^{\prime\prime\prime} = \left\lfloor {{y^{\prime\prime\prime}/\delta_{y}^{\prime\prime\prime}} + 0.5} \right\rfloor},} \end{matrix} \right. & (18) \end{matrix}$

where (δ″_(x), δ″_(y)) is an element size of this image.

C. Image Simulator in Discrete Space:

In discrete space, image simulator of the proposed compensator outputs the image CLP using the following 2 mappings f and f_(CLP).

$\begin{matrix} {{{f:I}->{DF}},{{{DF}\left( {x_{d}^{\prime},y_{d}^{\prime}} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} N} = 0} \\ {{\frac{1}{N}{\sum\limits_{i \in S}{I\left( {x_{d\mspace{11mu} i},y_{d\mspace{11mu} i}} \right)}}},} & {{{{if}\mspace{14mu} N} \geq 1},} \end{matrix} \right.}} & (19) \end{matrix}$

where N is the number of members in a set S, and coordinates (x′_(d), y′_(d)) are calculated from the corresponding coordinates (x_(d i), y_(d i)) {iεS|(x′_(d)≦x′_(di)≦x′_(d)+1)∩(y′_(d)≦y′_(di)≦y′_(d)+1)} using Equs. (1)-(5). Note that the N is not constant in each coordinates (x′_(d), y′_(d)).

$\begin{matrix} {{{f_{CLP}:{DF}}->{CLP}},{{{CLP}\left( {\eta_{d},\xi_{d}} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} N_{CLP}} = 0} \\ {{\frac{1}{N_{CLP}}{\sum\limits_{i \in S_{CLP}}{{DF}\left( {x_{d\mspace{11mu} i}^{\prime},y_{d\mspace{11mu} i}^{\prime}} \right)}}},} & {{{{if}\mspace{14mu} N_{CLP}} \geq 1},} \end{matrix} \right.}} & (20) \end{matrix}$

where N_(CLP) is the number of members in a set S_(CLP), and (η_(d), ξ_(d)) are calculated from the corresponding coordinates

(x′ _(di) ,y′ _(di)){iεS _(CLP)|(η_(d)≦η_(di)≦η_(d)+1)∩(ξ_(d)≦ξ_(di)<ξ_(d)+1)}.

The mapping f is foveation and the f_(CLP) is a kind of cortical mapping. It is noted that the proposed image simulator also calculates intensity of each point from corresponding N-multiple points.

D. Estimation:

For the following simulation, a boundary between fovea and para-fovea is defined by r₀=r_(max)c₁f₁ tan θ₀ using the AdWAF model. FIG. 3( a) shows a target image I of 512×512 [pixels], and FIG. 3( b) shows the distorted foveated image DF of 128×128 [pixels], simulated from the image I using mapping f when r_(max)=36 [pixels] in the AdWAF model in three cases of eccentricity, θ_(ε)=0[°], 18.69[°], and 34.08[°] (under conditions of δ=δ_(x)=δ_(y) and φ_(ε)=0). In order to test the compensator, two types of polar images P₁ and P₂ of 128×64 [pixels] in FIGS. 3( c) and (d), respectively, are used for comparison. The P₁ has polar coordinates (r′, φ′) around the origin of the image DF. The image P₂ has polar coordinates (r(θ), φ), on the image DF, around the optical axis of the fovea sensor. FIG. 3( e) shows the compensated log-polar image CLP of 128×64 [pixels] that corresponds to the para-fovea by Equ. (7), when θ₁=9.826 [°] and θ₂=19.107 [°]. The compensator corrects deformation of the log-polar image in each eccentricity θ_(ε), although the resolution of the image CLP distributes radial-asymmetrically. It should be noted that this comparison supposes a practical use of the fovea sensor such as a fovea lens and a log-polar chip. Especially, the case of the image P₂ indicates error of gaze position in an active manner. In order to understand the work of the compensator better, the undistorted foveated image UDF is also shown in FIG. 3( f). Note a resolution of the UDF image does not only decreases as going to the eccentricity gets larger but also it is space-variant.

The image CLP is estimated using root mean square error (RMSE) from a model log-polar image LP, acquired from the image I (FIG. 4). The RMSE E is represented by

$\begin{matrix} {{E = \sqrt{\frac{{{{CLP} - {LP}}}^{2}}{N_{\eta} \cdot N_{\xi}}}},} & (21) \end{matrix}$

where N_(η) and N_(ξ) show the size of the log-polar image.

FIG. 5 shows a comparison of RMSE among the polar images P₁ (broken line), P₂ (dashed line) and the image CLP (solid line) versus the eccentricity θ_(ε), when φ_(ε)=0. The RMSE of the image CLP is smaller in all incident angles than those of the images P₁ and P₂, and it increases gradually. This means that the compensator corrects the deformation well compared to the images P₁ and P₂. However, because intensity of more pixels in the image CLP is determined from the low-resolution part (such as the periphery of the image DF), the RMSE increases, as the eccentricity gets larger. The RMSE of the image CLP changes by up to 2.54[%], in condition of the eccentricity within 34.08[°].

The log-polar image acquired from a uniform-resolution image such as the image I is space-variant inherently, however, its resolution changes radial-symmetrically (that is, it does not change in the η-direction but changes only in the ξ-direction). When a low-pass filter is used for such space-variant image in order to reduce noise, not only noise but desirable information is also lost from the original image. In addition, if the eccentricity exists, resolution of the image CLP is not only space-variant but also radial-asymmetrical. FIG. 6 shows distribution of digitized errors, Δη and Δξ, of the image CLP, that means resolution in the η-direction and the ξ-direction, when θ_(ε)=0[°], 18.69[°], and 34.08[°] (when φ_(ε)=0). The errors Δη and Δξ are calculated from Δθ and Δφ, that is, digitized errors of the image DF in the radial and tangential directions, respectively. They are defined as

$\begin{matrix} \left\{ \begin{matrix} {{\Delta\eta} = {{{\frac{\partial\eta}{\partial\theta}} \cdot {\Delta\theta}} + {{\frac{\partial\eta}{\partial\varphi}} \cdot {\Delta\varphi}}}} \\ {{\Delta\xi} = {{{\frac{\partial\xi}{\partial\theta}} \cdot {\Delta\theta}} + {{\frac{\partial\xi}{\partial\varphi}} \cdot {{\Delta\varphi}.}}}} \end{matrix} \right. & (22) \end{matrix}$

II. Unreliable Feature Omission: A. Definition:

An Unreliable Feature Omission (UFO) is described below. If the image CLP is up-sampled from low-resolution part of the image DF (not fulfilling the sampling theorem), aliasing occurs as noise. This noise appears in components more than some frequency. The UFO discards such components using Discrete Wavelet Transform (DWT), because it is suitable for local noise reduction from the space-variant image. UFO is applicable for the radial-asymmetric space-variant resolution caused by the eccentricity, mentioned in the previous chapter. UFO is defined as follows:

1) Define DWT of the image CLP as ω. Its coefficient is represented as ω_(j k,i) ^(c), where j is resolution level and c shows diagonal, horizontal and vertical components as d, h, and v, respectively.

2) If points in the image, corresponding to each coefficient ω_(j k,i) ^(c), fulfill conditions ĤΞ, H and Ξ in each case of c=d, h and v, respectively, discard the coefficient (set it as zero) as to determine a matrix ν of wavelet coefficients.

$\begin{matrix} {{H:{{{\Delta\eta}\left( {{\frac{k \cdot N_{\eta}}{2^{j - 1}} + m},{\frac{2{l \cdot N_{\xi}}}{2^{j - 1}} + n}} \right)} > \frac{4\pi \; M_{f}}{2^{j - 1}}}}{{\Xi:{{{\Delta\xi}\left( {{\frac{k \cdot N_{\eta}}{2^{j - 1}} + m},{\frac{2{l \cdot N_{\xi}}}{2^{j - 1}} + n}} \right)} > \frac{4{N_{\xi} \cdot M_{f}}}{2^{j - 1}}}},}} & (23) \end{matrix}$

where M_(f) is a parameter regulating accuracy of the digitized error in sub-pixel order. The m and n are integer determined by the resolution level j.

3) Define an image U by Invert Discrete Wavelet Transform (IDWT) of the ν.

B. Estimation:

The image CLP is represented as DF+e1→^(f) ^(CLP) CLP when white Gaussian noise (WGN) e is added to the image DF. FIG. 7 shows the RMSE of the image before UFO (by a broken line), the image U after UFO (by Haar wavelet by a solid line) and the image U after UFO by Daubechies (7, 9) biorthogonal wavelet (by a solid line with circle) versus the eccentricity θ_(ε), (when φ_(ε)=0) (The RMSE is calculated from the image LP). This simulation uses condition of N_(η)=2N_(ξ)=128, and M_(f)=1.0. FIGS. 7( a), (b) and (c) show cases when the WGN is 0[%], 5[%] and 10[%], respectively. The RMSE is calculated by an average of 100 times repetition. The result shows that UFO by Daubechies (7,9) wavelet reduces the RMSE. Even in the case without the WGN, it reduces the RMSE by up to 0.40[%], under a condition of the eccentricity within 34.08[°]. This result means UFO works well for separating the original signal and noise locally based on inherent resolution of the space-variant image. UFO reduces the RMSE more as the WGN gets larger. This may be a desirable property of UFO.

In the case of Haar wavelet, however, when the WGN is 0[%], the RMSE after UFO is slightly larger than that that before UFO. This means that the high level coefficients, removed by UFO, include not only noise but also a part of the original signal. There may be two possible reasons as follows. One is that the distribution of the errors Δη and Δξ is not approximated sufficiently using only dyadic pyramid. The other is that actual errors of Δη and Δξ are smaller than estimated errors (by Equ. (22)), because the image CLP uses a sort of moving average when it is remapped from the image DF as defined in Equ. (20). FIG. 9 shows the RMSE when the regulating parameter M_(f) changes from 1.0 to 1.5. The RMSE when M_(f)=1.5 gets much closer to that before UFO than that when M_(f)=1.0, although it is not smaller than that before UFO. This result is in line with the possibilities enumerated above.

A combination of fovea sensor and compensator is described as follows. By taking account of combining the fovea sensor and the eccentricity compensator, a mapping f′_(CF) from the image DF to the compensated foveated image CF and a mapping f_(CF) from the image I to the image CF are defined as below:

$\begin{matrix} {{{f_{CF}^{\prime}:{DF}}->{CF}},{{{CF}\left( {x_{d}^{\prime\prime\prime},y_{d}^{\prime\prime\prime}} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} N_{CF}^{\prime}} = 0} \\ {{\frac{1}{N_{CF}^{\prime}}{\sum\limits_{i \in S_{CF}^{\prime}}{{DF}\left( {x_{d\mspace{11mu} i}^{\prime},y_{d\mspace{11mu} i}^{\prime}} \right)}}},} & {{{{if}\mspace{14mu} N_{CF}^{\prime}} \geq 1},} \end{matrix} \right.}} & (24) \end{matrix}$

where N_(CF) is the number of members in a set S′_(CF), and (x′″_(d), y′″_(d)) are calculated from the corresponding coordinates (x′_(d i), y′_(d i)) {iεS′_(CF)|(x′_(d)≦x′_(di)≦x′_(d)+1)∩(y′_(d)≦y′_(di)≦y′_(d)+1)}.

$\begin{matrix} {{{f_{CF}:I}->{CF}},{{{CF}\left( {x_{d}^{\prime\prime\prime},y_{d}^{\prime\prime\prime}} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} N_{CF}} = 0} \\ {{\frac{1}{N_{CF}}{\sum\limits_{i \in S_{CF}}{I\left( {x_{d\mspace{11mu} i},y_{d\mspace{11mu} i}} \right)}}},} & {{{{if}\mspace{14mu} N_{CF}} \geq 1},} \end{matrix} \right.}} & (25) \end{matrix}$

where N_(CF) is the number of members in a set S_(CF), and (x′″_(d), y′″_(d)) are calculated from the corresponding coordinates

(x _(di) ,y _(di)){iεS _(CF)|(x _(d) ≦x _(di) ≦x _(d)+1)∩(y _(d) ≦y _(di) ≦y _(d)+1)}

FIG. 10 compares these 2 mappings by changing the eccentricity θ_(ε)(=0, 18.69 and 34.08[°]) when φ_(ε)=0. The CF by the mapping f_(CF) has radial-asymmetrical space-variant resolution. On the other hand, the mapping f_(CF) gives us almost the same look in all cases of eccentricity, although a right-side boundary line changes slightly (because this simulation assumes the fovea sensor has a fixed 120-degree field of view). It should be noted that the mapping f_(CF) is a specific case when the image CF is acquired directly from an undistorted wide-angle image. Thus, the mapping f_(CF) is equivalent with the foveation after translation. This comparison of these two mappings indicates how the compensator should be combined with the fovea sensor in order to acquire a more robust RST-invariant feature.

III. Eccentricity Estimator:

A. Estimating Eccentricity from Distorted Foveated image:

Eccentricity Estimator (EE) estimates the eccentricity θ_(ε). from the image DF using a method by which rotation-, scale- and translation-invariant (RST-invariant) features are processed. Fourier-Mellin Transform (FMT) is well-known for extracting such a feature from a linear-coordinate image, that is, a Cartesian image. FMT is based on Fourier Transform (FT) theory. It is equivalent with FT of log-polar mapping (with RS-invariant property) from magnitude of FT (with translation-invariant property) of an image.

When FMT is applied to the image DF, its image distortion needs to be removed. FIG. 3( f) shows an undistorted foveated image UDF with Cartesian coordinates of 128×128 [pixels] in each eccentricity. The image UDF is calculated from the image DF by removing distortion (by Equ. (12) and Equ. (13)).

The discrete image UDF are simulated by a mappings f_(UDF) as follows:

$\begin{matrix} {{{f_{UDF}:{DF}}->{UDF}},{{{UDF}\left( {x_{d}^{''},y_{d}^{''}} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} N_{UDF}} = 0} \\ {{\frac{1}{N_{UDF}}{\sum\limits_{i \in S_{UDF}}{{DF}\left( {x_{d\mspace{11mu} i}^{\prime},y_{d\mspace{11mu} i}^{\prime}} \right)}}},} & {{{{if}\mspace{14mu} N_{UDF}} \geq 1},} \end{matrix} \right.}} & (26) \end{matrix}$

where N is the number of members in a set S_(UDF), and (x″_(d), y″_(d)) are calculated from the corresponding coordinates

(x′ _(di) ,y′ _(di)) {iε=S _(UDF)|(x″ _(d) ≦x″ _(di) ≦x″ _(d)+1)∩(y″ _(d) ≦y″ _(di) ≦y″ _(d)+1)}.

B. Applying FMT to UDF

The image DF is a space-variant image, that is, distorted. Thus, watermark-like alias is seen in the image UDF of FIG. 3( f) when the distortion is removed. It gets more remarkable as the eccentricity gets larger. Such aliasing occurs when the image is up-sampled from lower resolution (e.g., peripheral FOV of the image DF) to high-resolution (high-density space of the image UDF).

FIG. 11 shows a flowchart for RST-invariant feature acquisition from the image DF.

FIG. 12 shows a processing flow of FMT, where FMT M of N_(η)×N_(ξ) is acquired from an image signal, f, of N_(x)×N_(y). Reconstruction f_(re) is calculated from F_(ip), that is DFT in the log-polar space. FIG. 12 shows a case when the model reconstruction f_(m) _(—re) is calculated from the central part of the image I.

A model FMT M_(m) is acquired from the central part of the image I.

The eccentricity θ_(ε) is estimated from translation calculated between M and M_(m) using Equ. (3).

C. Estimation

The resolution of image DF is not uniform but it changes radial-symmetrically around the optical axis. That is, it does not change in the tangential direction in case of the same incident angle θ, but it changes only in the radial direction. When the θ_(ε) changes, the resolution of the image UDF gives the following properties:

1) The resolution changes largely versus the θ_(ε) (i.e., the resolution gets much lower than the case of θ_(ε)=0, as the θ_(ε) gets larger).

2) The resolution is space-variant, i.e., the resolution is not uniform in the whole of image. Also, it is radial-asymmetrical around the image center of the image UDF that corresponds to the target image center, if the θ_(ε) exists.

FMT of the image UDF is estimated using root mean square error (RMSE) between the reconstruction f_(re) and the model reconstruction f_(m) _(—) _(re) by the same size of N_(x)×N_(y). The RMSE E is denoted as

$\begin{matrix} {E = {\sqrt{\frac{{{f_{re} - f_{m\_ re}}}^{2}}{N_{x} \cdot N_{y}}}.}} & (27) \end{matrix}$

FIG. 13 shows the RMSE versus the eccentricity θ_(ε) (when φ_(d)=0) in three cases of N_(η)×N_(ξ)=256×128, 128×64, and 64×32. The RMSE is calculated by an average of 100 repetitions. In this simulation, the image DF is calculated in condition of r_(max)=36[pixel], θ_(max)=60.0[°], θ₀=9.826[°], θ₁=19.107[°] and θ₂=34.715[°]. The image UDF is mapped from the image DF in condition of r_(max)=128[pixel] when N_(x)=N_(y)=128. The RMSE of all three cases increases as the |θ_(ε)| gets larger. This is caused by the property of 1), that is, because intensity of more pixels in the image is determined from lower-resolution part. Under a condition of the eccentricity within 34.08[°], the RMSE changes by up to 3.17[%], up to 3.07[%] and up to 2.43[%] in the three cases, respectively. In addition to that, the RMSE is not zero (4.07[%], 3.14[%] and 2.08[%], respectively), even if θ_(ε)=0 (that is, by the property of 2)). The RMSE, in the case of N_(η)×N_(ξ)=64×32, is the smallest. This means FMT with small space of the N_(η)×N_(ξ) works as a low-pass filter. In other words, this case gives feature that is the robust but less detailed.

IV. Applying UFO for Eccentricity Estimation (EE):

Unreliable Feature Omission (UFO) is applied for local noise reduction of alias in the image UDF. This case applies UFO as follows:

If points in the image, corresponding to each wavelet coefficient ω_(j k,i) ^(c), fulfill conditions X̂Y, X and Y in each case of c=d, h and ν, respectively, discard the coefficient (set it as zero) to determine a matrix ν of wavelet coefficients and an image U by Inverse Discrete Wavelet Transform (IDWT) of the ν is defined.

$\begin{matrix} {{X:{{\Delta \; {x\left( {{\frac{k \cdot N_{x}}{2^{j - 1}} + m},{\frac{l \cdot N_{y}}{2^{j - 1}} + n}} \right)}} > \frac{4{N_{x} \cdot M_{f}}}{2^{j - 1}}}}{{Y:{{\Delta \; {y\left( {{\frac{k \cdot N_{x}}{2^{j - 1}} + m},{\frac{l \cdot N_{y}}{2^{j - 1}} + n}} \right)}} > \frac{4{N_{y} \cdot M_{f}}}{2^{j - 1}}}},}} & (28) \end{matrix}$

where Δx(x, y) and Δy(x, y) are digitized errors of the image UDF, calculated from Δθ and Δφ, that is, digitized errors determined from the image DF by Equ. (29), M_(f) is a parameter regulating accuracy of the digitized error in sub-pixel order. The m and n are integers determined by the resolution level j.

$\begin{matrix} \left\{ \begin{matrix} {{\Delta \; x} = {{{\frac{\partial x}{\partial\theta}} \cdot {\Delta\theta}} + {{\frac{\partial x}{\partial\varphi}} \cdot {\Delta\varphi}}}} \\ {{\Delta \; y} = {{{\frac{\partial y}{\partial\theta}} \cdot {\Delta\theta}} + {{\frac{\partial y}{\partial\varphi}} \cdot {{\Delta\varphi}.}}}} \end{matrix} \right. & (29) \end{matrix}$

FIG. 14 shows the RMSE versus the θ_(ε) (when φ_(ε)=0), of the image UDF (a broken line), the image U (a solid line), the reconstruction f_(re) of the image UDF (a broken line with square) and the reconstruction f_(re) of the image U (a solid line with circle). The RMSE is an average of 100 repetitions. The images DF uses the condition of r_(max)=36[pixel], θ_(max)=60.0[°], θ₀=9.826 [°], θ₁=19.107[°] and θ₂=34.715[°]. The image UDF is mapped from the image DF in condition of r_(max)=128[pixel] when N_(x)=N_(y)=128. UFO uses Daubechies (7, 9) biorthogonal wavelet in conditions of N_(x)=N_(y)=128, and M_(f)=1.0. With respect to FMT, two cases of N_(η)×N_(ξ)=256×128 and N_(η)×N_(ξ)=64×32 are compared in FIGS. 14( a) and (b). Note the following:

1) The RMSE of the U is always smaller than that of the image UDF. This means that UFO works well as an anti-alias filter that reduces local noise from the radial-asymmetric space-variant image, basically (comparing FIGS. 14( a) and (b), watermark-like alias is reduced). The RMSE is reduced by up to 0.58[%] and up to 0.65[%], in the cases of N_(η)×N_(ξ)=256×128 and N_(η)×N_(ξ)=64×32, respectively.

2) FIG. 14( a) proves that UFO improves a performance of FMT. UFO reduces the RMSE by up to 0.47[%] in this condition. In addition, the RMSE of the f_(re) of the image U changes by up to 2.93 [%]. Note that this value is smaller than 3.17 [%] before UFO. This also indicates that UFO removes alias well locally from the space-variant image (see FIGS. 15( c) and (d)).

3) FIG. 14( b) shows UFO does not work as well when the log-polar space is too small. As shown in FIGS. 15( e) and (f), the low-pass filter effect of FMT does not only reduce aliasing but also removes high frequency components of original signal of the images UDF and U.

Inherently, FMT is robust to changing resolution, because it has a property to extract scale-invariant feature. But if the resolution is space-invariant, that is, not uniform in the whole of the image, the performance is affected. Overall, UFO works well for FMT as an anti-aliasing filter. Because UFO does not reduce the original signal more than necessary, it is more suitable for keeping the feature(s) as robust and detailed as possible, compared to a global low-pass filter that impacts the entire image, for example, by making the log-polar space of FMT be smaller.

FIG. 16 illustrates a flowchart of a method 1600 of the present disclosure. The method 1600 could include providing a target image (block 1602), calculating a distorted image from the target image (block 1604), estimating an eccentricity between the target image and the distorted image (block 1606), compensating for the eccentricity between the target image and the distorted image (block 1608), modeling a distorted foveated image (block 1610), generating a log-polar image (block 1612), and omitting an unreliable feature (block 1614).

In the method 1600, the modeling the distorted foveated image may include sectoring a field of view in a plurality of areas. The plurality of the areas may at least be a foveal area, a para-foveal area, a near-peripheral area, and a peripheral area.

In the method 1600, the generating the log-polar image could include calculating at least one discrete coordinate. Further, the generating the log-polar image may include calculating at least one Cartesian coordinate. Still further, the compensating for the eccentricity may include performing a mapping.

In the method 1600, the performing the mapping could include at least one mapping selected from the group made of image to distorted foveated image and distorted foveated image to compensated log-polar image. Further, the at least one mapping of distorted foveated image to compensated log-polar image could include estimating a root mean square error. Still further, the omitting the unreliable feature may include generating a discrete wavelet transform, the omitting the unreliable feature could also include generating a threshold based on at least one quantity selected from the group made of a parameter regulating an accuracy of a digitized error, and an integer representing a resolution level.

Additionally, the omitting the unreliable feature may include generating an inverse discrete wavelet transform, and the omitting the unreliable feature may also include applying a Fourier-Mellin transform.

The foregoing method 1600 or elements of the method 1600 could also be stored on a computer-readable medium having computer-executable instructions to implement the method 1600 or the elements of the method 1600.

As a person having ordinary skill in the art would appreciate, the elements or blocks of the methods described above could take place at the same time or in an order different from the described order.

FIG. 17 illustrates a block diagram of an exemplary embodiment of a system 1700 of the present disclosure. The system 1700 may include a target image processing element 1702, a distorted image calculating element 1704 coupled to the target image processing element 1702, an eccentricity estimator 1706 coupled to the distorted image calculating element 1704, an eccentricity compensator 1708 coupled to the distorted image calculating element 1704, a distorted foveated image modeler 1710 coupled to the eccentricity compensator 1708, a log-polar image generator 1712 coupled to the eccentricity compensator 1708, and an unreliable feature omitter 1714 coupled to the eccentricity compensator 1708.

In the system 1700, the distorted foveated image modeler 1710 may be configured to sector a field of view in a plurality of areas. Such plurality of areas may be selected from: a foveal area, a para-foveal area, a near-peripheral area, and a peripheral area. Further, in the system 1700, the log-polar image generator 1712 may be configured to calculate at least one discrete coordinate. Further, the log-polar image generator 1712 could be configured to calculate at least one Cartesian coordinate. Further, as pertaining to the log-polar image generator 1712, the eccentricity compensator 1708 may be configured to perform a mapping. Such mapping may include at least one mapping selected from: image to distorted foveated image and distorted foveated image to compensated log-polar image. Further, the at least one mapping of distorted foveated image to compensated log-polar image could include a root mean square error estimation.

The unreliable feature omitter 1714 may be configured to generate a discrete wavelet transform. The unreliable feature omitter 1714 may be configured to generate a threshold based on at least one quantity selected from: a parameter regulating an accuracy of a digitized error, and an integer representing a resolution level.

The unreliable feature omitter 1714 could be configured to generate an inverse discrete wavelet transform. The unreliable feature omitter 1714 could be configured to apply a Fourier-Mellin transform.

As used in this specification and appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the specification clearly indicates otherwise. The term “plurality” includes two or more referents unless the specification clearly indicates otherwise. Further, unless described otherwise, all technical and scientific terms used herein have meanings commonly understood by a person having ordinary skill in the art to which the disclosure pertains.

It should be emphasized that the above-described embodiments are merely some possible examples of implementation, set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiments of the invention without departing substantially from the principles of the invention. All such modifications and variations are intended to be included herein within the scope of this disclosure and the present invention and protected by the following claims. 

1. A method of processing an image, the method comprising: providing a target image; calculating a distorted image from the target image; estimating an eccentricity between the target image and the distorted image; compensating for the eccentricity between the target image and the distorted image; modeling a distorted foveated image; generating a log-polar image; and omitting an unreliable feature.
 2. The method of claim 1, wherein the modeling the distorted foveated image includes sectoring a field of view in a plurality of areas.
 3. The method of claim 2, wherein the sectoring the field of view in the plurality of areas further includes sectoring the field of view at least in an area selected from the group consisting of: a foveal area, a para-foveal area, a near-peripheral area, and a peripheral area.
 4. The method of claim 1, wherein the generating the log-polar image includes calculating at least one discrete coordinate.
 5. The method of claim 4, wherein the generating the log-polar image includes calculating at least one Cartesian coordinate.
 6. The method of claim 1, wherein the compensating for the eccentricity includes performing a mapping.
 7. The method of claim 6, wherein the performing the mapping includes at least one mapping selected from the group consisting of: image to distorted foveated image and distorted foveated image to compensated log-polar image.
 8. The method of claim 7, wherein the at least one mapping of distorted foveated image to compensated log-polar image includes estimating a root mean square error.
 9. The method of claim 1, wherein the omitting the unreliable feature includes generating a discrete wavelet transform.
 10. The method of claim 1, wherein the omitting the unreliable feature includes generating a threshold based on at least one quantity selected from the group consisting of: a parameter regulating an accuracy of a digitized error, and an integer representing a resolution level.
 11. The method of claim 1, wherein the omitting the unreliable feature includes generating an inverse discrete wavelet transform.
 12. The method of claim 1, wherein the omitting the unreliable feature includes applying a Fourier-Mellin transform.
 13. A system for processing an image, comprising: a target image processing element; a distorted image calculating element coupled to the target image processing element; an eccentricity estimator coupled to the distorted image calculating element; an eccentricity compensator coupled to the distorted image calculating element; a distorted foveated image modeler coupled to the eccentricity compensator; a log-polar image generator coupled to the eccentricity compensator; and an unreliable feature omitter coupled to the eccentricity compensator.
 14. The system of claim 13, wherein the distorted foveated image modeler is configured to sector a field of view in a plurality of areas.
 15. The system of claim 14, wherein one of the plurality of areas is selected from the group consisting of: a foveal area, a para-foveal area, a near-peripheral area, and a peripheral area.
 16. The system of claim 13, wherein the log-polar image generator is configured to calculate at least one discrete coordinate.
 17. The system of claim 16, wherein the log-polar image generator is configured to calculate at least one Cartesian coordinate.
 18. The system of claim 13, wherein the eccentricity compensator is configured to perform a mapping.
 19. The system of claim 18, wherein the mapping includes at least one mapping selected from the group consisting of: an image to a distorted foveated image and a distorted foveated image to a compensated log-polar image.
 20. The system of claim 19, wherein the at least one mapping of the distorted foveated image to the compensated log-polar image includes a root mean square error estimation.
 21. The system of claim 13, wherein the unreliable feature omitter is configured to generate a discrete wavelet transform.
 22. The system of claim 13, wherein the unreliable feature omitter is configured to generate a threshold based on at least one quantity selected from the group consisting of: a parameter regulating an accuracy of a digitized error, and an integer representing a resolution level.
 23. The system of claim 13, wherein the unreliable feature omitter is configured to generate an inverse discrete wavelet transform.
 24. The system of claim 13, wherein the unreliable feature omitter is configured to apply a Fourier-Mellin transform.
 25. A computer-readable medium having computer-executable instructions for: providing a target image; calculating a distorted image from the target image; estimating an eccentricity between the target image and the distorted image; compensating for the eccentricity between the target image and the distorted image; modeling a distorted foveated image; generating a log-polar image; and omitting an unreliable feature. 